La nuova architettura di classe enterprise
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This document provides an agenda for an event titled "La nuova architettura di classe Enterprise" (The new Enterprise class architecture) taking place on July 5th, 2016 in Milan, Italy. The agenda includes introductions from 18:30-18:45, a discussion on how data architectures have changed since relational databases from 18:45-19:30, a case study from 19:30-20:00, and a discussion on how companies are benefiting from MongoDB support from 20:00-20:15. It also lists the contact information for the event lead, Valerio Bianchi.
La nuova architettura di classe enterprise
- 1. La nuova architettura di classe Enterprise Milano, 5 Luglio 2016 { name : "Valerio Bianchi”, title : ”EAE, Italy", phone : "+39-334-6403398", email : "[email protected]”, location : "Milan, Italy” }
- 2. 18:30–18:45 Introduzione:Cosae’cambiatodallanascitadeidatabaserelazionali 18:45–19:30 CreazionediunamodernaEnterpriseDataArchitecture 19:30–20:00 CaseStudy: CERVED,ajourneyofinnovation 20:00–20:15 ComeleaziendeleadernelmondostannobeneficiandodelsupportodiMongoDB 20:15–21:30 Cena:DiscussioneeNetworking
- 3. Strong Consistency Enterprise Mgmt & Integrations 1979 First relational DB gets released Expressive Query Language & Secondary Indexes
- 7. Martin “Marty” Cooper And The first cell phone, Dyna-Tac 8000X
- 8. IBM 1981
- 9. Refrigerator 2.52 GB 250 IBM 3380 1st HDD over 1 GB
- 10. The World Has Changed
- 11. DIGITAL ECONOMY Data Risk Time Cost
- 12. 90% of the data has been created in the last two years 80% of enterprise data are unstructured 2x Unstructured data is growing at a double rate Millions of users on global scale New Pay- for-value models Agile vs. Waterfall
- 13. Relational Model
- 14. Document Model
- 15. RDBMS The Power of the Document Model Document Model
- 16. Scale up Costs up Performance Down RDBMS Scalability, Costs and Performance
- 17. Scalability, Performance and Cost … Linear cost Linear Performance Commodity or in Cloud … Scale-out Distribute Data Where It Needs To Be
- 18. Object-oriented programming Document Model 1:1 Easier and Faster Development
- 21. Relational Always On, Global Scale Flexibility Scalability & Performance Expressive Query Language & Secondary Indexes Strong Consistency Enterprise Management & Integrations
- 22. Always On, Global Scale Flexibility Scalability & Performance Expressive Query Language & Secondary Indexes Strong Consistency Enterprise Management & Integrations NoSQL
- 23. Nexus Architecture Relational NoSQL Expressive Query Language & Secondary Indexes Strong Consistency Enterprise Management & Integrations Flexibility Scalability & Performance Always On, Global Scale
- 24. Operational Database LandscapeOperational Database Landscape
- 25. One DB to cover more apps RDBMSs Key/Value or Wide Column Stores MongoDB
- 26. RANK DBMS MODEL SCORE GROWTH (20 MO) 1. Oracle Relational DBMS 1,442 -5% 2. MySQL Relational DBMS 1,294 2% 3. Microsoft SQL Server Relational DBMS 1,131 -10% 4. MongoDB Document Store 277 172% 5. PostgreSQL Relational DBMS 273 40% 6. DB2 Relational DBMS 201 11% 7. Microsoft Access Relational DBMS 146 -26% 8. Cassandra Wide Column 107 87% 9. SQLite Relational DBMS 105 19% Source: DB-engines database popularity rankings; May 2015 4th Most Popular Database
- 27. Common MongoDB Use Cases Single View Internet of Things Mobile Real-Time Analytics Catalog Personalization Content Management
- 28. Evidence
- 29. Problem Why MongoDB ResultsProblem Solution Results In 2012 Mizuho developed a mission- critical regulatory reporting application on Microsoft SQL Server Faced scalability and schema flexibility limits as the volume of trades increased Had to implement multiple transformations of the trade record to load it into the Financial Conduct Authority’s systems Migrated the application off of Microsoft SQL to MongoDB Enterprise Advanced MongoDB was able to concurrently run complex reports for the business, without service degradation Relied on MongoDB Inc.’s support, consulting and training to ensure successful migration Used Ops Manager for proactive monitoring and alerting MongoDB delivered faster with less code than the relational alternatives Despite the developers having no prior knowledge of MongoDB, team was able to deliver first project in just 3 months Mizuho is now putting MongoDB at the core of its digital transformation project Currently adding 2-3 GB of data every day Regulatory Reporting One of the world's largest financial institutions puts MongoDB as corner stone of digital transformation
- 30. Credit File Storage Migrated from Oracle RAC reduced cost by 75%, while increasing ability to deliver new features to B to C channels Problem Why MongoDB ResultsProblem Solution Results Personal Solutions LoB was not growing due to competition from freecreditreport.com; had to cut costs Still needed to deliver innovative features to the site How to modernize across the stack with the same technology? Moved all BLOB storage (metadata, images, .pdf) from Oracle RAC to MongoDB on x86 Implemented sharding for horizontal scalability and replication for HA Used MongoDB services for PoC to gain buy-in from business leadership Reduced BLOB storage costs from $8 per GB to $2 per GB – 75% savings Rolling new features to site weekly under agile sprints, keeping the site in sync with new business products MongoDB is new non-relational standard in the Equifax modernization portfolio
- 31. Content Management Migrates from MySQL to MongoDB on AWS, saving £2M and dramatically cutting project lead time Problem Why MongoDB ResultsProblem Solution Results Orange Digital web properties have 4.5M users on web and 2.3M users on mobile, across www.orange.co.uk, Orange World, and the Orange Business site, and other digital assets. MySQL reached scale ceiling Metadata management too challenging with relational model – targeting handsets, users, types of data, video, feeds, text and more Replatformed on MongoDB and migrated to AWS Flexible data model makes it substantially easier and more efficient to manage variety of metadata Sharding enables scalability and unrivaled performance Supports 115,000+ queries per second Saved £2M+ over 3 yrs. “Lead time for new implementations is cut massively” MongoDB is default choice for all new projects Eliminated 6B+ rows of attributes – instead creates single document per user / piece of content
- 32. Next Gen E-Commerce Built custom e-commerce platform on MongoDB in 8 Months Problem Why MongoDB ResultsProblem Solution Results Existing CMS / product catalog (V4) was beginning to show its age: outdated UI, poor customer experience Site scalability needed to be improved to address demands of their high growth business; SQL Server was not up to par Company had limited amount of time to complete site upgrade to Version 5 as they wanted everything to be ready in time for the holiday season Built a homegrown e-commerce platform on MongoDB, reusing components from their old platform and using MongoDB’s flexibility to easily add further customization Multi-data center replication and sharding for disaster recovery and scalability / high performance needs What would have been an 18 month project was completed in 8 months Excellent performance and site reliability improve the customer experience Developers and content team empowered with the flexibility that MongoDB provides
- 33. Problem Why MongoDB ResultsProblem Solution Results Proprietary solution with rigid data model slowed rate of new service introductions, impacting competitiveness Unable to scale as subscriber and service portfolio expanded High TCO incurred from proprietary hardware and software Built new customer data management platform on MongoDB Flexible data model enables dynamic schema modification to support new service introductions Automatic sharding to scale database as the business grows MongoDB platform scales to serve 12M customers, with 50% reduced cost per subscriber Streamlined and simplified systems allowing faster innovation and higher agility Migration to MongoDB completed in just 6 months Customer Data Mgt. Telco leader unifies customer experience, driving 50% lower cost and reduced churn
- 34. E-Commerce Scaled e-commerce startup to 200+ MongoDB instances and 50+ million users in 4 years Problem Why MongoDB ResultsProblem Solution Results E-commerce startup needed a data platform that could help them compete with Amazon and eBay Wanted to be able to deliver seamless feature rollouts frequently, scale elastically to handle unpredictable peaks in demand, and minimize downtime with minimal effort – all things that were not possible with their old database Migrated to MongoDB, using flexible data model to seamlessly add and remove new data types, fields, and features Native analytics enabled Wish to build personalization engine that engages users with right content at right time Sharding addressed hardware limitations of a single server without adding complexity to the application Wish is one of the top 100 apps in the App Store With MongoDB’s help, the platform grown to 50+ million users in 4 years High throughput: 100k+ ops / sec Company valuation has increased 567% from 2014-2015
- 35. Personalization Built personalization engine in 25% the time with 50% the team Problem Why MongoDB ResultsProblem Solution Results Needed personalization server that acts as the master storage for customer data. Originally built on Oracle (over 14 months) but it performed below expectations, did not scale, and cost too much New requirements made Oracle unusable – 40% more data, must reload entire data warehouse (22M customers) daily in small window – could not be met with Oracle Implemented on MongoDB, using flexible data model to easily bring in data from disparate customer data source systems Expressive query language made it possible to access customer records using any field Consulting and support significantly reduced upfront development and deployment costs New version of personalization engine was built on MongoDB in 25% the time with 50% the team Led to performance boosts of more than a magnitude Storage requirements decreased by 66%, lowering infrastructure costs
- 36. Single Platform for Financial Data Quantitative investment manager with over $11B in assets under management invests heavily in new database Problem Why MongoDB ResultsProblem Solution Results AHL needed new technologies to be more agile and gain competitive advantages in the systematic trading space Proprietary systems in financial services tech, as well as relational databases, were too expensive and/or rigid Built single platform for all financial data on MongoDB Flexible data model and scalability were core to ability to put all data in single platform Expressive query language, secondary indexes and strong consistency were core to ability to migrate core use cases to new platform 100x faster to retrieve data Tick Data: Quickly scaled to 250M ticks per second, a 25x improvement in tick throughput Cut disk storage 60%, and realized 40% cost savings by using commodity SSDs
- 37. Reference Data Management Major app migrated to MongoDB, saving $40M over 5 years Problem Why MongoDB ResultsProblem Solution Results Globally distributed app reference data management app did not meet SLAs required in delivering data to traders, resulting in SEC fines and damages to the reputation of firm Complex infrastructure with many components (i.e., ETL, caching, proprietary storage) was expensive and difficult to maintain Replatformed on MongoDB for a simplified infrastructure Native replication made it easy to replicate to data centers on multiple continents, bringing it closer to stakeholders and reducing the effects of geographic latency $40M in savings over 5 years with simplified infrastructure and the use of commodity servers Application in compliance with strict SLAs
- 38. Single View of Customer Insurance leader generates coveted single view of customers in 90 days – “The Wall” Problem Why MongoDB ResultsProblem Solution Results No single view of customer, leading to poor customer experience and churn 145 years of policy data, 70+ systems, 24 different 1-800 numbers, 15+ front- end apps that are not integrated Spent 2 years, $25M trying build single view with DB2 – failed Built “The Wall,” pulling in disparate data and serving single view to customer service reps in real time Flexible data model to aggregate disparate data into single data store Expressive query language and secondary indexes to serve any field in real time Prototyped in 2 weeks Deployed to production in 90 days Decreased churn and improved ability to upsell/cross-sell
- 39. 100+ Apps and Growing Faster time-to-market and lower operational overhead makes MongoDB the new default at Expedia Problem Why MongoDB ResultsProblem Solution Results Developers impeded by rigid relational model leading to inability to effectively keep up with business Significant effort achieving performance targets and maintaining optimal user experience Significant operational overhead involved in maintaining status quo, and time-to-deploy new systems Flexible data model makes it easy to adapt to unforeseen and frequent changes, allowing for radical data model changes with no downtime Inherent high-performance removed need for significant ongoing performance tuning Native HA and multi-DC support streamlined production deployments 100+ new apps launched over a couple of years Enabled Ops to provision new Production systems in under an hour: > 72X productivity improvement over SQL Server 1:100+ Ops Engineer to Production Server ratio compared to 1:28 with SQL Server
- 40. Risk Mgt. for the Connected Home Delivering on customer protection mission with MongoDB Problem Why MongoDB ResultsProblem Solution Results Need to create innovative apps that support corporate mission to protect customers, improve brand perception, and reduce churn Poor view of customers’ behavior at home, which can be used to provide more compelling pricing and better products Unable to offer a new experience around domestic connected objects Build a scalable mobile app allowing customers to integrate domestic connected objects to detect and prevent risk Leverages flexible data model to support specific APIs with third parties: Philips Hue light bulbs, NEST smoke detection, MyFox cameras, and more in fast evolving market Prototype built in 2 months; deployed in production in 4 months Able to prevent domestic risks and assist customers in case of incidents. Proactive alerting to customers minimizes their risk, creates customer loyalty Great user experience improves perception of AXA brand
- 41. Problem Why MongoDB Results Problem Solution Results AXA Banque needed to differentiate themselves in the the retail banking space by offering a 100% mobile experience Reaching the “digital native” demographic required new tools and approaches The solution must be able to absorb large peak loads and true 24/7/365 availability “We were quickly convinced that MongoDB's open-source NoSQL model was the best choice” Pascal Lozovoy CIO AXA Banque Mobile application is capable of absorbing large amounts of unstructured data with ease Seamless integration with existing banking platform on AS400 mainframe is achieved using Java and web services SooN successful launch Jan 2014 now has thousands of users New functionality is quickly developed and released to the public Zero interruption of service despite not limiting what users can upload Mobile Banking Platform AXABanqueusesMongoDBtopower“SooN”theirinnovativemobilebankingplatform
- 42. Kicking Out Oracle Global bank with 48M customers in 50 countries terminates $70M Oracle ULA, makes MongoDB database of choice Problem Why MongoDB ResultsProblem Solution Results Slow development cycles due to RDBMS’ rigid data model hindering ability to meet business demands High TCO for hardware, licenses, development, and support ($70M Oracle ULA) Poor overall performance of customer-facing and internal applications Building dozens of apps on MongoDB, both net new and migrations from Oracle – e.g., significant portion of retail banking, including customer-facing and backoffice apps, fraud detection, card activation, equity research content mgt.) Flexible data model to develop apps quickly and accommodate diverse data Ability to scale infrastructure and costs elastically Able to cancel $70M Oracle ULA. Evaluating what apps can be migrated to MongoDB. For new apps, MongoDB is default choice Apps built in weeks instead of months or years, e.g., ebanking app prototyped in 2 weeks and in production in 4 weeks 70% TCO reduction Tier 1 – European Global Bank
- 43. Internet of Things Leading solutions provider for smart energy networks enables new apps at 10x the speed and 10% of the cost Problem Why MongoDB ResultsProblem Solution Results Many utilities find that 70% of their enterprise data is generated from Smart Grid Networks, yet extracting that information is costly and resource intensive Utilities end up relying on siloed apps that are difficult to scale SilverSpring built SilverLink Sensor Network on MongoDB App seamlessly captures and stores high volumes of rapidly changing, IoT data from the energy grid Produces near real-time analytics of fast moving data across different parameters – temporal, geospatial, sensor type Allow utilities and app devs to extract actionable insights into energy usage patterns as they’re happening for better apps, better customer experiences Allows for the delivery of new applications and services for utilities and energy consumers at 10x the speed, and 10% of the cost
- 44. Database as a Service Telecom equipment manufacturer provides a scalable database as a service for internal applications Problem Why MongoDB ResultsProblem Solution Results Huawei had to support numerous internal applications and store associated big data, including logs and large attachments Previous relational solutions (DB2 and MySQL) lacked horizontal scalability, delivered poor performance, especially in long-running analytical queries, and made it difficult to manage unstructured data Huawei built the Huawei Application Engine (HAE) using MongoDB as the underlying database layer MongoDB scaled horizontally to meet the customer’s needs, and allowed them to distribute the cluster across data centers around the world The HAE easily handles unstructured and variably structured data from Huawei’s internal applications Accelerated app releases by allowing developers to focus on apps vs. ops Replaced multiple technologies with a single MongoDB deployment, reducing costs and operational complexity Improved concurrency and performance vs. previous RDBMS – with MongoDB 100K ops/sec; queries reduced from minutes to seconds
Editor's Notes
- #8: Sviluppato da Motorola, pesava 1130 grammi e non aveva display. The Motorola DynaTAC 8000X commercial portable cellular phone received approval from the U.S. FCC on September 21, 1983.[1] A full charge took roughly 10 hours, and it offered 30 minutes of talk time.[2] It also offered an LED display for dialing or recall of one of 30 phone numbers. It was priced at $3,995 in 1984, its commercial release year, worth a modern-day price of nearly $10,000. DynaTAC was an abbreviation of "Dynamic Adaptive Total Area Coverage."
- #10: IBM 3380 is the 1st HDD to break the 1 GB barrier in 1980
- #11: But of course the world has changed a lot since the 1980s when the relational database first came about. First of all, data and risk are significantly up. In terms of data 90% data created in last 2 years - think about that for a moment, of all the data ever created, 90% of it was in the last 2 years 80% of enterprise data is unstructured - this is data that doesn’t fit into the neat tables of a relational database Unstructured data is growing 2X rate of structured data At the same time, risks of running a database are higher than ever before. You are now faced with: More users - Apps have shifted from small internal departmental system with thousands of users to large external audiences with millions of users No downtime - It’s no longer the case that apps only need to be available during standard business hours. They must be up 24/7. All across the globe - your users are everywhere, and they are always connected On the other hand, time and costs are way down. There’s less time to build apps than ever before. You’re being asked to: Ship apps in a few months not years - Development methods have shifted from a waterfall process to an iterative process that ships new functionality in weeks and in some cases multiple times per day at companies like Facebook and Amazon. And costs are way down too. Companies want to: Pay for value over time - Companies have shifted to open-source business and SaaS models that allow them to pay for value over time Use cloud and commodity resources - to reduce the time to provision their infrastructure, and to lower their total cost of ownership
- #12: But of course the world has changed a lot since the 1980s when the relational database first came about. First of all, data and risk are significantly up. In terms of data 90% data created in last 2 years - think about that for a moment, of all the data ever created, 90% of it was in the last 2 years 80% of enterprise data is unstructured - this is data that doesn’t fit into the neat tables of a relational database Unstructured data is growing 2X rate of structured data At the same time, risks of running a database are higher than ever before. You are now faced with: More users - Apps have shifted from small internal departmental system with thousands of users to large external audiences with millions of users No downtime - It’s no longer the case that apps only need to be available during standard business hours. They must be up 24/7. All across the globe - your users are everywhere, and they are always connected On the other hand, time and costs are way down. There’s less time to build apps than ever before. You’re being asked to: Ship apps in a few months not years - Development methods have shifted from a waterfall process to an iterative process that ships new functionality in weeks and in some cases multiple times per day at companies like Facebook and Amazon. And costs are way down too. Companies want to: Pay for value over time - Companies have shifted to open-source business and SaaS models that allow them to pay for value over time Use cloud and commodity resources - to reduce the time to provision their infrastructure, and to lower their total cost of ownership
- #13: But of course the world has changed a lot since the 1980s when the relational database first came about. First of all, data and risk are significantly up. In terms of data 90% data created in last 2 years - think about that for a moment, of all the data ever created, 90% of it was in the last 2 years 80% of enterprise data is unstructured - this is data that doesn’t fit into the neat tables of a relational database Unstructured data is growing 2X rate of structured data At the same time, risks of running a database are higher than ever before. You are now faced with: More users - Apps have shifted from small internal departmental system with thousands of users to large external audiences with millions of users No downtime - It’s no longer the case that apps only need to be available during standard business hours. They must be up 24/7. All across the globe - your users are everywhere, and they are always connected On the other hand, time and costs are way down. There’s less time to build apps than ever before. You’re being asked to: Ship apps in a few months not years - Development methods have shifted from a waterfall process to an iterative process that ships new functionality in weeks and in some cases multiple times per day at companies like Facebook and Amazon. And costs are way down too. Companies want to: Pay for value over time - Companies have shifted to open-source business and SaaS models that allow them to pay for value over time Use cloud and commodity resources - to reduce the time to provision their infrastructure, and to lower their total cost of ownership
- #24: MongoDB was built to address the way the world has changed while preserving the core database capabilities required to build functional apps MongoDB is the only database that harnesses the innovations of NoSQL and maintains the foundation of relational databases
- #25: Let’s examine where the technology is positioned. Here are a few of the most popular types of persistence models in use today. RDBMS, being the most mature, are deep in functionality – but the legacy design principles are rooted on design principles almost 40 years old. And that comes at the expense of rich interaction with today’s programming languages, design requirements, and infrastructure implementation choices. Key-value stores, at the other end of the spectrum, act essentially like HashMaps (for those Java programmers in the audience) but are not really general purpoise databases. MongoDB trades some features of a relational database (joins, complex transactions) to enable greater scalability, flexibility, and performance for purpose. By that we mean performance for the operations as executed at the data access layer, not necessarily TPS at the database level.
- #30: Mizuho Industry: Financial Services, Banking Use Case: DBaaS
- #31: Equifax Industry: Financial Services, Consumer Credit Reporting Use Case: Content Management
- #32: Orange Use Case: Content Management Industry: Telecommunications
- #33: Under Armour Industry: Retail Use Case: Content Management, Product Catalog
- #34: Bouygues Use Case: Content Management Industry: Telecommunications
- #35: Wish, wish Industry: Retail Use Case: Personalization, Catalog, Content Management
- #36: Telefonica Industry: Telecommunications Use Case: Personalization
- #37: Man AHL Use Case: DBaaS, Tick Data, Many Industry: Financial Services
- #38: Citi Use Case: Content Management Industry: Financial Services
- #39: Metlife Industry: Insurance, Financial Services Use Case: Single View
- #40: Expedia Industry: Travel and Hospitality Use Case: Many Uses: Multi data center deployments
- #41: AXA Industry: Insurance, Financial Services Use Case: Mobile
- #44: Silver Spring, SilverSpring Use Case: IoT Industry: Energy, Technology
- #45: Huawei Use Case: DBaaS Industry: Telecom, Manufacturing